System and method for digital content development using a natural language interface

ABSTRACT

Aspects of the subject disclosure may include, for example, obtaining a natural language instruction, interpreting the instruction to obtain a machine interpretation, and analyzing the machine interpretation to obtain an intent of the natural language instruction. An actionable command adapted to cause a digital manipulation tool to digitally manipulate a content item is determined according to the intent of the natural language instruction. The actionable command is provided to the digital manipulation tool to obtain the manipulated content item. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/896,748, filed Jun. 9, 2020. All sections of the aforementionedapplication(s) and/or patent(s) are incorporated herein by reference intheir entirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a system and method for digitalcontent development using a natural language interface.

BACKGROUND

Digitally generated content is commonly utilized in media-basedapplications. Examples include animation, special effects, interactivevideo games, advertising, augmented reality (AR), virtual reality (VR),scientific visualization, and graphic design. With respect to visualapplications, digitally generated content may include one or more oftwo-dimensional (2D) content, three-dimensional (3D) content andanimated graphics.

Some digitally generated content may be developed according to digitalimages of real, or physical objects, e.g., using images obtained viadigital photography and/or digital videography. Other digitallygenerated content may be developed without restrictions of physicalobjects or environments. For example, imagery used in some video games,animation or even cinema may include realistic landscapes, objects, andactors. Alternatively or in addition, imagery may include imaginativesubject matter, limited only by the imagination of a content creator.The subject matter of digitally generated content may includelandscapes, such as land, sea, sky vegetation, inanimate objects, suchas buildings or vehicles and animals, including humanoids, real orimagined.

Digital content synthesis and/or manipulation may be accomplished viaone or more supporting software tools. Such tools may take the form ofapplication programs and/or services. Such tools may facilitate contentgeneration by one or more of user definition, user selection amongpreviously generated content elements, and application of computeralgorithms.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a digital content development system functioning withinthe communication network of FIG. 1 in accordance with various aspectsdescribed herein.

FIG. 2B depicts an illustrative embodiment of a digital contentdevelopment process in accordance with various aspects described herein.

FIG. 2C depicts an illustrative embodiment of another digital contentdevelopment process in accordance with various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

Unfortunately, digital content synthesis and/or manipulation, includingutilization of supporting tools, typically requires specialized trainingand/or skills. The subject disclosure describes, among other things,illustrative embodiments for obtaining a natural language instruction,analyzing it to obtain user intention, and formulating an actionablecommand according to the intention. The actionable command is adapted tocause a digital manipulation tool to digitally manipulate a content itemaccording to the natural language instruction. Beneficially, the digitalmanipulation tool is operable according to the natural languageinstruction, without requiring user training and/or familiarity withoperation of the digital manipulation tool. Other embodiments aredescribed in the subject disclosure.

One or more aspects of the subject disclosure include a process thatincludes receiving, by a processing system including a processor, anatural language instruction and generating a machine interpretation ofthe natural language instruction. The machine interpretation of thespoken, natural language instruction is analyzed, by the processingsystem, to obtain an intent of the spoken, natural language instruction.A first actionable command is identified, by the processing system,wherein the command is adapted to cause a first digital manipulationtool to digitally manipulate a content item according to the intent ofthe spoken, natural language instruction, to obtain a manipulatedcontent item. The first actionable command is provided, by theprocessing system, to the first digital manipulation tool to obtain themanipulated content item.

One or more aspects of the subject disclosure include a device,including a processing system having a processor and a memory thatstores executable instructions. The instructions, when executed by theprocessing system, facilitate performance of operations that includeobtaining an input signal including a natural language instruction. Theinput signal is interpreted to obtain a machine interpretation of thenatural language instruction. The machine interpretation of the naturallanguage instruction is analyzed to obtain an intent of the naturallanguage instruction. An actionable command adapted to cause a digitalmanipulation tool to digitally manipulate a content item is determinedaccording to the intent of the natural language instruction, to obtain amanipulated content item. The actionable command is sent to the digitalmanipulation tool to obtain the manipulated content item.

One or more aspects of the subject disclosure include a non-transitory,machine-readable medium, including executable instructions that, whenexecuted by a processing system including a processor, facilitateperformance of operations. The operations include obtaining a naturallanguage instruction and interpreting the natural language instructionto obtain a machine interpretation of the natural language instruction.The machine interpretation of the natural language instruction isanalyzed to obtain an intent of the natural language instruction. Anactionable command adapted to cause a digital manipulation tool todigitally manipulate a content item is determined according to theintent of the natural language instruction, to obtain a manipulatedcontent item. The actionable command is provided to the digitalmanipulation tool to obtain the manipulated content item.

Currently, the process of creating 2D and 3D interactive andnon-interactive or passive content requires the use of contentdevelopment tools that are manipulated or otherwise controlled via amouse, a keyboard, a pen, and/or touch interfaces, e.g., utilizing dragand drop features. Using tools for these types of content creationgenerally requires familiarity with specialized commands, userinterfaces and workflows, as well as proficiency in manipulating theinterfaces. Such steep learning curves can pose a significant barrier toentry to digital artistry and/or animation. Even after a user hasobtained a required degree of proficiency, the content creation processusing these tools can be extremely time consuming.

The demand for new 2D and 3D content is rising exponentially asdistribution accelerates with increasing wireless and wired internetspeeds, widening internet access availability, and the furtherproliferation of wired and wireless devices for experiencing bothinteractive and passive content. This tool accelerates the contentcreation process while also expanding its accessibility beyond contentcreation experts and specialists. As a result, the various aspectsdisclosed herein serve to fulfill the increasing demand for 2D and 3Dcontent that cannot be met by current toolchains and workflows. It is anobject of the techniques disclosed herein to reduce the learning curveas well as time required for creating 2D and 3D audio visual content.

In some embodiments, voice and/or text based natural language processingare adapted to provide a primary user interface that may be coupled witha procedural based content development systems. These systems mayfacilitate creation of new content and/or entity retrieval ofpreexisting content from content sources, such as content resourcelibraries and/or databases. A voice interface is provided as a primarymeans of interacting with one or more content development tools. Theinterface and the tool(s) can be applied to a wide variety of digitalcontent types and mediums including but not limited to games, movies,television, internet video, internet content, virtual reality, augmentedreality, mixed reality, and advertising content.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a system 100 in accordance withvarious aspects described herein. For example, the system 100 canfacilitate in whole or in part obtaining a natural language instructionand interpreting it to obtain a machine interpretation that is analyzedto obtain an intention of the natural language instruction. Anactionable command is determined according to the intention. Theactionable command is adapted to cause a digital manipulation tool todigitally manipulate a content item to obtain a manipulated content itemaccording to the natural language instruction. In particular, acommunications network 125 is presented for providing broadband access110 to a plurality of data terminals 114 via access terminal 112,wireless access 120 to a plurality of mobile devices 124 and vehicle 126via base station or access point 122, voice access 130 to a plurality oftelephony devices 134, via switching device 132 and/or media access 140to a plurality of audio/video display devices 144 via media terminal142. In addition, communication network 125 is coupled to one or morecontent sources 175 of audio, video, graphics, text, and/or other media.While broadband access 110, wireless access 120, voice access 130 andmedia access 140 are shown separately, one or more of these forms ofaccess can be combined to provide multiple access services to a singleclient device (e.g., mobile devices 124 can receive media content viamedia terminal 142, data terminal 114 can be provided voice access viaswitching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc., for facilitating the broadband access110, wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway, or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers, and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc., can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

In some embodiments, such as the example system 100, a natural languageprocessor 180 is in communication with one or more mobile devices 124,126 and/or static devices, e.g., 114, and/or media terminals 142. Thenatural language processor 180 may be adapted to facilitate in whole orin part interpretation of a natural language input to determine anintention regarding development of digital content according to thevarious techniques disclosed herein. In at least some embodiments, thenatural language processor 180 generates an actionable command todevelop digital content according to the intention of the naturallanguage command. Likewise, in at least some embodiments, such as theexample system 100, a digital content development tool, illustrated as acontent development server 182, is in communication with one or moremobile devices 124, 126 and/or static devices, e.g., 114, and/or mediaterminals 142, and/or other components of the communication network 125,e.g., one or more of the network elements 150, 152, 154, 156. In atleast some embodiments, the content development processor 180 may beadapted to facilitate in whole or in part development of digital contentaccording to the actionable commands.

In at least some embodiments, the mobile devices 124, 126 may includeresident functionality 184 a, 184 b, . . . 184 n, generally 184, e.g.,in the form of any one of an operating system, a client, a resident app,and combinations thereof. The resident functionality may be adapted toperform one or more of the techniques disclosed herein, such asinterpretation of the natural language input to determine intention,generation of an actionable command to develop digital content accordingto the intention, and/or development of digital content according to theactionable commands, as discussed further below. In at least someembodiments, the resident functionality 184 operations in cooperationwith external functionality, e.g., functionality of other mobile devicesand/or services and/or systems. For example, the resident functionalityprovides client functionality of a client-server arrangement, in whichserver functionality is provided by another device, such as the naturallanguage processor 180 and/or the content development server 182.

Likewise, the stationary devices 114 can be adapted with functionality186 a . . . 186 m, generally 186, and the media terminal 142 adaptedwith functionality 188, in the form of any one of an operating system, aclient, a resident app, and combinations thereof. The residentfunctionalities 186, 188 can be adapted to perform one or more of thetechniques disclosed herein, such as interpretation of the naturallanguage input to determine intention, generation of an actionablecommand to develop digital content according to the intention, and/ordevelopment of digital content according to the actionable commands. Inat least some embodiments, one or more of the functionalities 186, 188operate in cooperation with external functionality, e.g., functionalityof other devices and/or services and/or systems. For example, thefunctionality 186 and/or 188 may provide client functionality of aclient-server arrangement, in which server functionality is provided byanother device, such as the natural language processor 180 and/or thecontent development server 182. In some embodiments, the stationarydevices 114 and/or the media terminal 142 may include network enableddevices, such as smart appliances, and the like implementingmachine-type communications. It is envisioned that one or morestationary devices 114 and/or the media terminal 142 may be adapted tofacilitate one or more of the interpretation of the natural languageinput to determine intention, the generation of the one or moreactionable commands to develop digital content according to theintention, and/or the development of the digital content according tothe actionable commands. In some embodiments, functionalities of one ormore of the natural language processor 180 and/or the contentdevelopment processor 182 may be implemented in whole or in part on oneor more of the mobile devices 124, 126, on one or more of the stationarydevices 114 and/or one or more of the media terminals 142. Accordingly,it is envisioned that in at least some embodiments, one or more of thenatural language processor 180 and/or the content development processor182 may not be required, as their respective functionalities may beimplemented on other elements of the system 100, such as the networkelements 150, 152, 154, 156, the mobile terminals 124, 126, thestationary devices 114 and/or the media terminals 142.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a digital content development system 200 functioningwithin the communication network 100 of FIG. 1, in accordance withvarious aspects described herein. The system 200 may include a naturallanguage processor 210, adapted to process natural language input. Thenatural language input may include words spoken by a human, e.g., verbalutterances. In at least some embodiments, natural language input mayinclude paralinguistic features, such as facial expressions, laughter,eye contact, and gestures. For example, the natural language input mayinclude non-verbal utterances, such as a sigh and/or laughter. Othernon-verbal utterances may include, without limitation, “ugh,” “tsk,”“huh?”, “um,” and more generally, any utterances as may be used inconversational non-verbal behaviors. It is understood that determiningmeaning and/or intention from natural language input may depend at leastin part on generally accepted definitions of words and terms. It is alsounderstood that in at least some instances, meaning may be determinedfrom paralinguistic features, such as non-verbal utterances or gestures,either alone or on combination with verbal utterances. In someinstances, paralinguistic features, such as non-verbal utterances and/orgestures may contribute to any sentiment as may be used determiningmeaning. In at least some instances, meaning may be inferred based oncontext, e.g., prior input, a current project, information obtained froma user profile, such as age, gender, occupation, preferences.

In at least some embodiments, natural language input, e.g., includingutterances, verbal or otherwise, may be converted to a signal, e.g.,using a transducer, such as a microphone. Non-audible paralinguisticfeatures, such as gestures, may be obtained from other input devices,such as a camera or camera array and/or a touch screen or touchpad. Thesignal may include an analog signal component and/or a digital signalcomponent. The signal may include an electrical signal, such as atime-varying voltage and/or current, representative of the input.Digital signals may be obtained using analog-to-digital convertersadapted to convert an analog signal into a digital representation. In atleast some embodiments, the signals may be obtained from paralinguisticfeatures, such images and/or video clips of a user providing input tothe system 200. Alternatively or in addition, the input signals mayinclude alphanumeric values, e.g., determined according to gesturesentered at a touch-sensitive device, menu inputs and the like. It isconceivable that natural language input may be obtained directly fromutterances of a human, and/or via audio, video, or audiovisualrecordings. It is also conceivable that natural language input may beobtained from synthesized words, e.g., words uttered by a machine.

It is understood that natural language input includes words spoken by ahuman, paralinguistic cues, recordings of words and/or paralinguisticcues and synthesized words or speech. It is understood further thatnatural language input may include input obtained from a user inputdevice, such as a keyboard, keypad, pointing device, such as a mouse orstylus. Such natural language input may include text, e.g., entered bythe keyboard, and/or machine-readable symbols obtained via the inputdevice, e.g., as may be generated responsive to a user selection of amenu item.

The natural language processor 210 may be adapted to determine anintention from the input and to generate one or more instructions and/oractionable commands responsive to the intention. The natural languageprocessor 210 may be in communication with one or more contentdevelopment tools 213. The content development tool(s) 213 may beadapted to develop digital content, e.g., by one or more of creation,modification, manipulation, combination, transformation, animation,actuation for interactive play, integration with other digital content,presentation, and the like. The actionable command(s), when provided bythe natural language processor 210 to the development tool(s) 213, causethe tool(s) to implement one or more actions according to the determinedintention. Implementation of the commands cause the tool(s) 213 tocreate, modify, combine, transform, animate, actuate, integrate, and/orotherwise manipulate digital content to fulfill the perceived intentionof the user input.

The example system 200 may include a storage device or storage system214 in communication with one or more of the natural language processor210 and the content development tool 213. The storage system 214 may,alone and/or together with the content development tool 213, provide asearchable repository of content items and may be integrated with thesystem 200 and/or at least remotely accessible to the natural languageprocessor 210, e.g., via network connectivity. The storage system 214may be adapted to store, organize, catalogue and/or retrieve one or moredigital content items. By way of example, the content developmenttool(s) 213 may include a selection tool adapted to select digitalcontent items from the storage system 214. Alternatively or in addition,the content development tool(s) 213 may include a content generationtool adapted to generate and/or otherwise create digital content itemsthat may be stored in the storage system 214 for archiving and/or laterretrieval. Content items may include, without limitation, completescenes, elements of scenes, such as landscape items, e.g., hills,mountains, lakes, clouds, objects within a scene, such as buildings,vehicles, trees, and/or actors, such as animals, including people.

One or more of the natural language processor 210 and the contentdevelopment tool 213 may be in further communication with a contentprocessor 215. The content processor 215 may be adapted to processdigital content, such as digital content items enacted upon by thecontent development tool(s) 213, digital work products that incorporateone or more of the digital content items enacted upon by the tool(s)213, such as video content, audio content, audio visual content, passivecontent, interactive content, such as computer games, and the like. Suchprocessing may include preparation for presentation on a display device216.

In some embodiments, the content processor provides a workspace or workenvironment adapted to present digital content items that may be indevelopment and/or developed according to the natural language processor210 and/or the one or more content development tools 213. Processing ofthe digital content items may include, without limitation, assembly ofone or more digital content items into a composite scene, rendering ofone or more of the digital content items and/or at least a portion ofthe composite scene, animation of one or more elements of the sceneand/or characters within the scene. Other elements of a scene, such aslighting, camera angles, viewing perspectives, and the like may beimplemented by one or more of the content development tool(s) 213, thenatural language processor 210 and/or the content processor 215. Theprocessed output provided by the content processor 215 may include arendered frame or group of frames, e.g., according to an animation.Alternatively or in addition, processed output may include one or morescenes and/or actors suitable for incorporation into gameplay of acomputer game.

According to the techniques disclosed herein, natural language input,such as spoken instructions, may be used to create video. Naturallanguage input may indicate “create a video scene,” e.g., followed bysubsequent instruction to further development of a video scene. Suchinstructions may identify a location of a scene, e.g., a city, a naturalscene, such as a meadow or a mountain, outer space, under water and soon. For example, a user may speak “create object X in background Y.”Such an instruction is an example of a compound instruction that mayresult in generation of a first group of actionable commands adapted tocreate a background Y according to a first digital content developmenttool. Likewise, the same compound instruction may result in generationof a second group of actionable commands adapted to create the object Xaccording to the first digital development tool, or perhaps a differentdigital content development tool. It is conceivable that such a compoundinstruction may result in yet a third group of actionable commandsadapted to position the object X, once created, into the scene Y, toobtain an approximation of the user's original request.

It is understood that in at least some embodiments, the system 200 mayinclude logic and/or rules adapted to process compound instructions,and/or to parse out and/or to otherwise determine sub-instructions,e.g., to generate X, and to generate Y, and to combine X and Y, oncegenerated. Such logic and/or rules may be adapted to determine anordering and/or sequencing in which the sub-instructions should beimplemented. For example, the logic and/or rules may identify one ormore dependencies, such that a first sub-instruction that depends upon aresult of a second sub-instruction is applied only after a response tothe second sub-instruction is obtained.

Alternatively or in addition, such instructions may identify a styleand/or format of the scene, objects or actors within a scene, and/oranimation as may be applied to the scene and/or objects or actors withina scene. Example styles may include 2D animation, 3D animation,realistic, cartoonish, and so on. The particular digital developmenttool selected and/or actionable commands determined in response to theinput may depend upon identified styles, genres, samples, e.g.,“generate a scene like ‘Star Wars’,” or generate a scene similar to the“Simpsons.” According to the first example, tools and/or actionablecommands are adapted to provide realistic scenes, objects, charactersand/or actions, while in the latter, they may be adapted to providecartoonish scenes, characters, and/or actions, e.g., according tocoloring, level of detail and so on.

In at least some embodiments, one or more instructions may be inferredfrom the natural language input, e.g., the spoken instruction. Forexample, the natural language input may indicate “create a Western-stylescene,” or “create an alien-world scene.” Inference may be drawn that aWestern-style scene may include a particular landscapes, such as adesert, a mountain range, one or two-story wooden buildings, characters,such as cowboys, styles of clothing, objects, such as horses and cactusand the like. Thus, from the command “create a Western-style scene”inferences can be drawn as to particular features and/or objects and/orcharacters of the scene. In response to such inferences, an originalintention to create the scene can be mapped to a more detailed group ofinferred intentions to create one or more of the features and/or objectsand/or characters that may make up the intended scene. Actionablecommands may then be generated according to the more detailed group ofinferred intentions.

It is understood that in at least some applications, different tools maybe used for different aspects of the originally intended scene.Continuing with the Western-style scene example, a first group ofinterred intentions to create a landscape and/or backdrop may use analgorithm-based tool to generate a sandscape, mountains and/or dessertsky features. Likewise, a second group of inferred intentions to createa character in the scene may use a selection tool adapted to select acharacter from a stock of characters stored in a library and/or databaseresource. To the extent one or more of the natural language intentionand/or inferred intentions neglect to identify parameters as may berequired to comply with the instruction, estimates may be made. Forexample, estimates may be made regarding features, such as lighting,camera position for video, scale, angle, graphic interaction rules,e.g., how physics may be modeled in animations, color palate, imagefiltering, and so on. Alternatively or in addition, the system 200 maybe adapted to request user clarification, e.g., via supplemental userinput, before completing a response to the natural language input, suchthat tool selection and/or the particular actionable commands may beadapted to comply with clarification obtained by supplemental userinput. Alternatively or in addition, the system 200 may be adapted torequest a user response, e.g., via supplemental user input, aftercompleting a response to at least a portion of the natural languageinput, e.g., the first group of interred actions. The supplemental inputmay indicate acceptance or rejection of a result obtained according tosystem selections, estimations and/or guesses applied to one or moreparameters. In response to a rejection, the system may prompt the userto provide supplemental user input to provide further detail,clarification, and/or correction. Any supplemental content may beprocessed, resulting in one or more of a tool selection and/or theparticular actionable commands may be adapted to comply withclarification obtained by supplemental user input.

In at least some embodiments, the content processor 215 may be used toprovide feedback to a user, such as visual representations ofimplementations of intentions obtained responsive to the naturallanguage input. Such feedback may occur periodically, e.g., responsiveto completion of a task, a project, and/or upon a request by the user.Alternatively or in addition, such feedback may occur in real time, ornear-real time. In this manner, a user may observe results of animplementation of determined intention. Such presented results mayrepresent valuable feedback to confirm an implementation and/or tocorrect and/or further adjust a digital content work product as may benecessary. It is understood that in at least some embodiments, thenatural language processor 210 may provide to the content processor 215output for presentation on the display other than the digital contentwork product. For example, the output may include an indication of theuser input, e.g., text obtained from a speech-to-text processing of averbal input to allow a user to confirm accuracy of a verbal command.Alternatively or in addition, the output may include an indication ofthe determined intent and/or the actionable commands.

Actional command(s) generated natural language processor 210 may beadapted to develop digital content in cooperation with one or moredigital content development tools. For example, the natural languageprocessor 210 may generate any actionable command(s) responsive to adetermined intention of a natural language user input, wherein theactionable commands are adapted for a particular digital contentdevelopment tool. In at least some embodiments, the natural languageprocessor 210, in response to the same intention, may generate differentactionable command(s) depending upon a particular digital developmenttool towards which the actionable command(s) are directed.

In at least some embodiments, feedback provided by the content processor215 and/or the display device 216 may be used as a training tool.Feedback may include one or more of a natural language input, adetermined intent and/or actionable commands. Such a training tool maybe adapted to familiarize a user with operation of the one or moredigital development tools 213, by exposition of the actionable commandsalone or in combination with one or more of the determined intent andthe natural language input. Alternatively or in addition, such atraining tool may facilitate improvement to natural language inputs byassociating one or more of a content development tool response, and/or adetermined intent with a natural language input. A user may observewhich natural language inputs produced desirable and/or undesirableresults, which may lead to modified natural language inputs in order tobetter achieve operation of the system 200.

It is understood that the natural language processor 210 may includemachine learning. Machine learning may be applied to one or more of userinput, including natural language input, determination of intentionsfrom the user input and/or generation of actionable commands responsiveto the intentions and/or user input. Machine learning may be appliedgenerally to input received from multiple users and/or restricted to asingle user or group of users, e.g., to personalize performance of thesystem 200. Alternatively or in addition, machine learning may beapplied to one or more categories of users and/or content developmenttool(s) 213, e.g., to improve performance of the system by one or moreof accuracy or efficiency. In at least some embodiments, machinelearning may be applied to particular tasks as may be related to thedigital content development process.

Machine learning may use a training set associated with a particularuser, or a group of users. For example, the machine learning may use atraining set obtained from interactions with other users. The otherusers may be related to the particular user and/or other users that maynot be related, but share similar characteristics, interests, and/orbrowsing histories. For example, machine learning may be applied toindividuals of a particular creative company, such as individuals of aparticular studio, or a particular animation studio, and/or a particulargame developer. Alternatively or in addition, machine learning may beapplied to classes of users, such as, cartoonists, animators, gamedevelopers.

The natural language processor 210 may include one or more of a userinterface 217, an interpreter module 218, a command module 211 and in atleast some embodiments, a tool access module 221. The user interface 217may be adapted to accept or otherwise receive input from a user. Theuser input may include one or more of audio input, e.g., uservocalizations and/or visual input, such as still images and/or video.For example, the user interface 217 may include a microphone, amicrophone array, and/or a camera system including a digital cameraand/or a digital camera array. The microphone may be adapted to recorduser sounds, including verbal and non-verbal utterances of the user. Inat least some embodiments, the microphone may be adapted to captureambient sounds. The camera system may be adapted to capture one or moreof a user's physical gestures, body position, expression, such as facialexpression, body language, ambient conditions, such as lighting, soundlevels, detection of other individuals, and so on.

Alternatively or in addition, the user interface 217 may be adapted toreceive one or more of textual input and/or user selections responsiveto user prompts. For example, the user interface 217 may include one ormore of a keyboard or keypad, a touchpad, a touch screen, a pointingdevice, such as a mouse, a joystick, a trackball, and the like. Textualinput may be obtained via user entry of typewritten commands at akeyboard and/or keypad and/or selection of a prompt, such as an image, aword or collection of words, a phrase, and so on, e.g., upon a graphicalinterface of the user interface 217. Still other forms of user input mayinclude gesture-based input, e.g., according to user manipulation of atouchpad of the user interface 217 and/or according to a camera systemadapted to observe a position and/or motion of at least a portion of auser's body, such as a position of a finger or hand, or a movement of afinger or fingers, a hand or hands, forearm, wrist, head, torso, legs,objects manipulated by a user, such as a stylus, a baton, a ball, anarticle of clothing, such as a shirt, a jacket, a hat, a glove, and thelike.

It is understood that in at least some embodiments, user input mayinclude multimodal input, such as combinations of one or more of audioinput, e.g., speech, visual input, e.g., user position and/or movements,textual input and/or input from one or more pointing devices. Thenatural language processing may be applied to one or more of the variousmodes of input, such as vocalizations alone, vocalizations incombination with one or more of visual cues obtained from the userand/or an environment of the user, textual input and/or input obtainedfrom a pointing device.

The interpreter module 218 may be adapted to process input obtained fromthe user interface 217 according to one or more of the various modes ofuser input. In some embodiments, the interpreter module 218 may includea translator adapted to generate machine-readable input from one or moreof the various modes of user input. For example, the interpreter module218 may include a speech-to-text processor adapted to generate textrepresentative of a vocal input obtained via a microphone system.Alternatively or in addition, the interpreter module 218 may include anaudio processor adapted to interpret at least some aspects of a user'svocalizations, converting them into a machine-readable form. Forexample, certain spoken words, phrases, sentences received via themicrophone system result in electrical signals having characteristics,such as amplitude response, frequency response, intensity, and so on. Itis understood that in at least some instances one or morecharacteristics of the electrical signals may be recognizable as a word,phrase, or sentence. For example, such electrical signals or parts ofsignals may be compared and/or otherwise correlated to a reference, suchas a library of signals, to determine candidate matches. It isconceivable that the interpreter module 218 may arrive at aninterpretation of a user input, or at least a portion of the user input,according to analysis of an electrical signal obtained from a vocalinput without using a speech-to-text processor.

Interpretations of a user input may be provided to the command module211 for further processing. The command module 211 may include ananalyzer module 219 and an instruction generator 220. The command module211 may receive interpretations from the interpreter 218 and analyze theinterpreted input alone or in combination with the interpreter 218 todetermine one or more intentions associated with the user input.Analysis performed by the analyzer module 219 may include, withoutlimitation, application of one or more of a parser, a dictionary and/orgrammar. It is understood that the extent of intentions may be limitedby one or more of a general application of digital content development,a particular application of digital content development, such asanimation, and/or one or more of the content development tool(s) 213.

Consider a dictionary and/or grammar defined, at least in part, bypredetermined processes associated with digital content development. Forexample, common instructions may include a bounded group of words orphrases generally related to the digital development process, such asinsert, move, delete, resize, copy, paste, rotate, animate, illuminate,texturize, and so on. Alternatively or in addition, instructions may bedetermined according to predefined instructions and/or applicationprogramming interfaces (API) of a particular tool or group of tools. Inaddition to interpreting individual words or phrases, instructions mayadhere to a predetermined grammar, e.g., including an action to beimplemented by one of the content development tools 213 and an object tobe enacted upon by the tool(s).

The interpreter module 218 and/or the analyzer module 219 may includeassociations of variations in expression of user input, withpredetermined intentions and/or commands, e.g., to allow the interpretermodule 218 and/or the analyzer module 219 to synthesize a relativelyfree-form, or natural language input, into a predetermined command. Itis understood that such associations may be predetermined, e.g.,pre-loaded into the interpreter and/or analyzer module 219.Alternatively or in addition, such associations may be identified,modified, and/or refined based on user input. For example, theinterpreter module 218 and/or the analyzer module 219 may apply machinelearning, e.g., identifying associations of natural language input withdetermined intentions that required corrections and/or those that didnot require corrections. Modifications may be made to the associationsas appropriate based on identifications of correct and/or incorrectprior determined intentions.

Adaptation of the command(s) may include formulating commands thatutilize a finite set of predetermined commands, the commandscorresponding to a particular tool towards which the commands aredirected. It is understood that an intention determined from the naturallanguage user input, e.g., texturize an object, may lead to a firstactionable command, when directed towards a first digital developmenttool configured to texturize an object, while leading to a second,different actionable command, when directed towards a second digitaldevelopment tool also configured to texturize an object.

In at least some embodiments, the analyzer module 219 may receive atleast a portion of user input directly from the user interface 217. Forexample, verbal and/or visual cues may be forwarded to the interpretermodule 218, while one or more non-verbal and/or non-visual cues, such astext, menu selections and the like are provided directly to the analyzermodule 219. It is understood that in at least some embodiments, one ormore of the interpreter module 218 and/or the analyzer module 219 mayprovide input to the user interface 217. By way of example, and withoutlimitation, such input may take a form of a prompt direct towards theuser, a menu of selectable options, a query based on received userinput, and so on. Such queries may be directed to correction orclarification, e.g., “Did you mean . . . ?” alternatively or inaddition, it is understood that some commands properly interpreted maylead to inquiries direct towards elaboration and/or furtherclarification. Consider a user request to insert an actor, e.g., asoldier, into a scene. The analyzer module 219 having determined anintention to insert the soldier may require further intention, e.g., asto whether the soldier would be generated by the content developmenttool(s) 213, i.e., a new soldier, or whether the soldier would beselected from a library resource of stored soldiers. Perhaps the inquirymay ask which library to use, and/or other features, such as libraryaccess costs, type of soldier, e.g., revolutionary war era, civil warera, etc.

The instruction generator 220 may be adapted to generate one or moreinstructions based on one or more intentions received from the analyzermodule 219. The instruction generator 220 may be adapted to generateinstruction(s) according to one or more particular content developmenttools 213. It is understood that many currently available contentdevelopment tools 213 require a certain degree of familiarity and/orproficiency, e.g., as may be garnered by prior experience and/ortraining. It is also understood that such experience and/or training mayserve as a barrier to entry for many users. In at least someembodiments, the instruction generator 220 may be preconfigured withassociations of intentions with actionable instructions. Thus, when anintention and/or group of intentions is received from the analyzermodule 219, the instruction generator 220 may identify actionableinstructions according to the predetermined associations.

In at least some embodiments, the particular actionable instructions maydepend on which content development tool 213 will be employed infulfilling the intention. Consider the instruction generator 220 havinga first group of predetermined associations of intentions and actionablecommands according to a first development tool and a second group ofpredetermined associations of intentions and actionable commandsaccording to a second development tool. To the extent the instructiongenerator 220 is aware of which development tool will be employed, itmay choose the appropriate group of predetermined associations. Thus,the actionable commands will be configured and/or otherwise adapted forpresentation to the appropriate tool 213, which may proceed to enactupon the actionable commands to develop digital content according to thedetermined intention.

The example natural language processor 210 includes a tool access module221 in communication with the command module 211, e.g., via one or moreof the instruction generator 220 and/or the analyzer module 219. In someembodiments, the tool access module 221 is adapted to identify aparticular content development tool 213 in association with a particularintention and/or group of actionable commands generated responsive tothe particular intention. The particular tool 213 may be a default tooldetermined according to system configuration, user preferences, priortool association and/or access rights and/or credentials. Userpreferences may include a user choice identified by a user profileand/or obtained via the user interface 217, e.g., via natural languageinput and/or user selection from a presented list of accessible tools213. User preferences may be limited by tool access license agreements,user access restrictions, subscription levels, and the like.

In at least some embodiments, the tool access module 221 facilitatesidentification of a particular content development tool 213. Forexample, the analyzer module 219 having determined one or moreintentions responsive to the natural language input, may implement logicto identify a particular tool 213 according to one or more of the natureof the intention and/or user authorization and/or access rights. In someembodiments, the analyzer module 219 provides an indication of theselected tool 213 to the instruction generator 220 and/or the toolaccess module 221. The instruction generator 220 may apply anappropriate association based on the identified tool in order to obtainactionable commands suitable for the identified tool 213.

Alternatively or in addition, the analyzer module 219 may require userinput to resolve any ambiguity in selection of the appropriate tool 213.For example, a determined intention may be carried out by more than onecontent development tool 213. It is conceivable that the analyzer module219, the tool access module 221 and/or a combination thereof mayimplement rules and/or logic to select from among a list of multipletools 213. The rules and/or logic may be adapted to make such selectionsaccording to one or more of cost, quality, latency, licenserestrictions, authorizations, prior experiences, and the like. In atleast some embodiments, the analyzer module 219 and/or the tool accessmodule 221 may initiate a prompt to a user via the user interface 217requesting additional user input to facilitate tool identification. Suchuser prompts may include one or more of identifying availablealternatives, identifying other factors, such as costs, tool sources,ratings, user reviews, and the like. Having identified a target contentdevelopment tool 213, the tool access module 221 may be adapted toforward to the tool 213 the actionable commands received from theinstruction generator 220.

The digital content development tools may include, without limitation,machine-readable instructions, e.g., computer software, for use in oneor more of creating animation, rendering animation, executing animation,displaying animation, visual effects, video games, computer games,virtual reality, augmented reality, digital media content. The contentdevelopment tools may include proprietary tools and/or commerciallyavailable tools. By way of illustrative example and without limitation,the tools may include graphic modeling tools, such as TurboSquid, adigital media company that sells stock 3D models used in 3D graphics;SpeedTree, a group of vegetation programming and modeling softwareproducts developed and sold by Interactive Data Visualization, Inc.Alternatively or in addition, the tools may include animation tools,such as Blender, an open-source 3D computer graphics software toolsetused for creating animated films, visual effects, art, 3D printedmodels, motion graphics, interactive 3D applications, and computergames. Other example animation tools include 3D studio Max, aprofessional 3D computer graphics program for making 3D animations,models, games and images, developed and produced by Autodesk Media andEntertainment, and MAYA®, a registered trademark of Autodesk, Inc.,Mountain View, Calif., for computer software for use in creating,rendering, executing and displaying animation, visual effects, video andcomputer games, and digital media content).

In at least some embodiments, the content development tools may includegame engines, such as the Unreal Engine, a game engine developed by EpicGames, and/or the Unreal Development Kit, for creating and/or modifyingcomputer games. More generally, at least some content development toolsmay be adapted primarily for game developers, while others may beadapted primarily for end users, e.g., consumer-oriented tools as may beemployed by game players in association with game play. One example of aconsumer-oriented tool is the “Dreams” user-generated content gameavailable from Media Molecule, Guildford, U.K., which provides aconsumer-oriented experience for creating games and other contentincluding movies and music. Other non-limiting example classes ofcontent creation tools may include tools that exist within games such asFortnite and Minecraft. Access to tools and features within games, suchas Fortnite and Minecraft, may be integrated with the techniquesdisclosed herein, e.g., as integrated via one or more of a plugin, anAPI, a software development kit (SDK) or other technique to provideusers with capabilities to extend and modify a game experience and/or tomodify and/or to add content to it.

Still other games may provide lower-level “modding” tools that may beintegrated with and/or otherwise interfaced by the systems, devices,processes and techniques disclosed herein. Examples include the “HalfLife” video game, developed by Valve and published by Sierra Studios andthe “Quake” video game developed by id Software and published by GTInteractive, first-person shooter video games, and the “Fallout” videogame developed and published by Interplay Productions. Such “modding”tools may be used extensively by a creator community to change or extendthe original experiences provided by the games originating developmentstudio. It is envisioned that one or more of the techniques discloseherein may be applied to such example games and/or tools to facilitateoperation and/or access to such games and/or tools by developers andend-users alike.

Another example of a game engine tool is Unity, a cross-platform gameengine developed by Unity Technologies. Such game engines may be used tocreate three-dimensional, two-dimensional, virtual reality, andaugmented reality games, as well as simulations and other experiences.Game engines may include terrain engines for detailed 3D environments,lighting effects, such as real-time dynamic shadows, directional lightsand spotlights, particle effects, video playback. For example, thesystem may interpret natural language commands and generate commandsbased on the interpretation according to an associated development tool,e.g., using Unreal Engine's native scripting language used for authoringgame code and gameplay events, Unity's Scriptable Render Pipelineallowing developers to create high-end graphics, and/or the WolframLanguage, from Unity, for accessing high-level functions of the Wolframlanguage.

When combined with speech-to-text processing, the natural languageprocessing may be used to process a human vocal utterance into a contentcreation command or content modification command. A content creationcommand may be sent to a downstream fulfillment service that can eitherretrieve an existing variant of the content in a library or to a servicethat uses procedural methods to generate the content. The retrieved ornewly generated content may then be sent and/or otherwise applied to acontent scene. A modification command may be sent and/or otherwiseapplied directly to the content scene. At any time, the user may exportthe content scene in a portable interactive or non-interactive format,application, or package in variants for different devices and platforms.At any time, the use may also choose to use a traditional input methodsuch as mouse, keyboards, touch, or pen to apply modifications to thescene as method of fine grain adjustment.

In operation, the tool access module 221 receives the actionable commandfrom the instruction generator 220. The intent determined from thenatural langue user input is adapted according to a predeterminedcommand structure of an identified development tool 213 or class oftools. The instruction generator 220 and/or tool access module 221implement the actionable command to generate and/or modify a digitalcontent item, resulting in a generated and/or modified digital contentitem according to the actionable command. In at least some embodiments,the generated and/or modified digital content item is provided to thecontent processor 215. The content processor 215 may perform one or moreof an encoding, a decoding, and a rendering of the generated and/ormodified content item to obtain a presentable form of the generatedand/or modified digital content item. In at least some embodiments, thepresentable form is provided to the display device 216 for presentation.

It is understood that in at least some embodiments, a presentation atthe display device 216 may be observed by the user, e.g., to inspect thegenerated and/or modified digital content item that resulted from theoriginal natural language input. The user, in turn, may accept theresult by providing a confirmatory command or providing no correctiveinstruction by natural language input or otherwise. Should the userdetermine, however, that the result was unsatisfactory, e.g., observingthat the user's intention was interpreted improperly, or that aconsequence of a proper interpretation and implementation of the naturalcommand did not meet the user's objectives, the user may provide asubsequent natural language instruction to initiate a corrective action.In at least some embodiments, the subsequent instruction may include arequest to undo or otherwise unwind an instruction, such as theimmediately previous instruction or group of instructions. In suchinstances, the system 200 may be adapted to simply undo the or unwindthe identified instruction(s), e.g., deleting a newly generated contentitem, or undoing an associated modification to an existing digitalcontent item. Alternatively or in addition, the system 200 may beadapted to further generate and/or modify the resulting modifiedcontent, as may be required.

The system and processes disclosed herein are adapted to facilitaterapid creation of 2D and/or 3D audio, visual, and/or audiovisual,interactive and non-interactive content consisting of, but limited to,characters, objects, and environments along with their behaviors,appearance, and other properties. Tools 213 and libraries 214 exist forassisting in rapidly creating 2D and 3D audio visual content. However,none are known which use voice as the primary interface. Speedtree isone popular tool for procedurally creating digital terrain. Turbosquidand Sketchfab are two popular 3D content libraries where 3D models canbe purchased pre-made. Tools exist to automate music creation byspecifying style, tempo, and other parameters. Various audio, music, andvideo libraries both online and offline provide a method of purchasingstock sound and video clips for projects. Text-to-speech systems (TTS)are widely available for creating synthetic voice sequences. Microsoft's“neural TTS” product is a recent available service for this purpose.Some service providers may offer commercially available libraries, e.g.,mimicking and/or otherwise approximating natural voices, that could beused to create realistic text to speech sequences. As vendors,Microsoft, Google, Amazon, Nuance, and other companies offer naturallanguage processing systems that customers can customize to predictintent or meaning from provided text sequences. Middleware authoringplatforms such as Unity and Unreal provide methods of authoring contentthat can be exported to run as applications on a variety of devicetypes. In at least some applications, one or more of the various thenatural language processing techniques disclosed herein may be packagedas a plugin adapted to extend functionality of a digital developmentplatform, such as Unity and Unreal.

Beneficially, the techniques disclosed herein may be applied to simplifyprocesses of creating, developing, and/or otherwise manipulating 2D and3D interactive and non-interactive content generation by reducingcomplexity, time required, and cognitive load. In at least someembodiments, voice inputs are received, interpreted, and/or adapted tominimize a cognitive load. Such systems and/or processes may be appliedto increase an amount of digital content available for monetization,and/or to reduce one or more of content creation, content acquisition,and/or licensing costs.

FIG. 2B depicts an illustrative embodiment of a digital contentdevelopment process 250 in accordance with various aspects describedherein. The process 250 includes receiving user input at 251. The userinput may include natural language input, e.g., a word, phrase, sentenceand/or sentences. It can be presumed in at least some instances that thenatural language input relates to a command or instruction to furtherdevelopment of a digital content item, such as a scene, an object oritem within a scene, an actor, animation, gameplay, and the like. Thenatural language input may be received according to one or more of anaudio input, a visual input, e.g., by way of a still camera and/or avideo camera system, a textual input, a user selection according topresented options, gestures, and the like.

The received instruction may be interpreted at 252 to obtain a machineinterpretation. Interpretation may include speech-to-text processing forvocal instructions, e.g., translating spoken words to textualrepresentations. Alternatively or in addition, interpretation may bebased on characteristics of an audio signal obtained from a vocalinstruction. Characteristics may include, without restriction, one ormore of an amplitude response or a frequency response. For example, theinterpreter module 218 may compare or otherwise correlate one or more ofthe amplitude or frequency responses to a library of responsesassociated with machine interpretations of such responses, withoutnecessarily requiring a speech-to-text transformation.

The machine interpretation may be analyzed at 253 to obtain an intent ofnatural language instruction. For example, a machine-readable response,such as a textual response obtained from an interpretation of the inputcan be evaluated to determine an intention and/or intentions of theinput. Evaluation may include, without limitation, a parsing of theinput, an application of a grammar, and/or identification of key aspectsof the input, such as an object to be acted upon, an action to beundertaken in relation to the object, and the like.

In at least some embodiments, the process 250 optionally determines adigital content development tool at 254, shown in phantom. It isenvisioned that the process 250 may be employed in association with asingle digital content development tool, e.g., packaged as a front end,or an add-on feature of the tool. Alternatively or in addition, theprocess 250 may be employed in association with multiple tools, e.g., asan independent application or app adapted to interact with multipledifferent content development tools. According to the latter scenario,the process 250 is adapted to identify which tool or tools of a numberof available or possible tools should be associated with the user input.It is understood that a user may engage in a development session inwhich all or at least a group of user inputs will be directed to thesame tool. Thus, a determination of an appropriate tool may be made onceat 254 and applied to multiple inputs. Alternatively or in addition, adetermination of an appropriate tool may depend on the particular userinput. In such instances, a determination may be made at 254 for eachuser input and applied to the appropriate tool as determined.

An actionable command is identified at 255 according to the intent and atarget digital development tool. As disclosed herein, one ore moreactionable commands are determined responsive to the user input. Theactionable commands may be structured and/or otherwise scriptedaccording to an intention determined form the user input. It isunderstood that in at least some instances a set of possible commandsmay be bounded at least to some degree based upon the application todigital content development. Further restrictions to a set of possiblecommands may be bounded further based on a nature of a developmentproject and/or the particular development tasks being implemented. Forexample, if it is understood that the project and/or user inputs willrelate to animation, but not interactive content, then it can beinferred by the process 250 that the user input is related to animationand not to interactive content. In at least some embodiments, one ormore actionable commands and/or scripts of such commands can bepredetermined and associated with a list of possible intentions. In suchinstances, the process 250 having identified the intention at 252 mayrefer to the predetermined association to obtain the actionable commandsat 255. The actionable command is provided at 256 to the digitaldevelopment tool to obtain a digital content item created and/orotherwise manipulated according to the spoken natural languageinstruction obtained at 251.

It is envisioned that in at least some embodiments, the process 250 mayinclude interactive features, such as prompts to a user requestingclarification of an instruction received at 251 and/or confirmation thatan interpretation obtained at 252 is correct. For example, a determinedintention may be presented to a user via user interface in a format of aprompt for clarification and/or confirmation. Likewise, one or moreresults of

FIG. 2C depicts an illustrative embodiment of another digital contentdevelopment process 270 in accordance with various aspects describedherein. The process 270 includes obtaining user input at 271. The userinput may be obtained according to a natural language format accordingto any of the various input modes and techniques disclosed herein. Anintention of the natural language input may be determined at 272.Determination of the intention may be obtained by one or more of any ofthe various intention determining techniques disclosed herein. Adetermination is made at 273, as to whether the intention relates togeneration of a new digital content item or modification of an existingdigital content item. References to new and existing may be in referenceto a scene and/or project upon which a user is developing. For example,a preexisting digital content item may be requested or otherwiseobtained from an external source, such as a digital development tooland/or a digital content library. Thus, the media content item may beexisting in reference to the external source. However, an initialincorporation of the newly obtained digital content item into the sceneand/or project may be considered as a generation of the digital contentitem.

To the extent it is determined at 273 that the intention relates togeneration of a new digital content item, a digital content generationtool, service and/or source is identified at 274. Identification of thedigital content generation tool and/or service may include any of thevarious techniques disclosed herein. One or more actionable commands areidentified at 274 according to one or more of any of the varioustechniques disclosed herein. The one or more actionable commands areprovided at 275 to the identified tool, service and/or source, and agenerated digital content item is obtained at 277.

To the extent it is determined at 273 that the intention relates tomodification of an existing digital content item, a digital contentmodification tool, service and/or source is identified at 281.Identification of the digital content modification tool and/or servicemay include any of the various techniques disclosed herein. One or moreactionable commands are identified at 282, e.g., according to one ormore of any of the various techniques disclosed herein. The one or moreactionable commands are provided at 283 to the identified tool, serviceand/or source, and a modified digital content item is obtained at 284.

Having obtained a generated digital content item at 277 and/or amodified digital content item at 284, the process 270 progresses todetermine at 278 whether the generated and/or modified digital contentitem is animated and/or interactive. In at least some embodiments, adetermination whether the digital content is animated and/or interactivemay be made according to the particular content development toolemployed. Alternatively or in addition, such a determination may be madeaccording to one or more of the user input, the determined intent,and/or the actionable commands. It is envisioned that such adetermination may be facilitated by a user identity and/or a userprofile and/or by a response to a direct user inquiry requesting whetheranimation and/or interactivity should be applied.

To the extent it is determined at 278 that the content item is animatedand/or interactive, the content item is adapted at 279 for animationand/or interaction and the digital scene is updated at 280 according tothe animated and/or interactive content item. To the extent it isdetermined at 278 that the content item is neither animated norinteractive, the digital scene is updated at 280 according to thegenerated and/or modified digital content item.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIGS. 2B and2C, it is to be understood and appreciated that the claimed subjectmatter is not limited by the order of the blocks, as some blocks mayoccur in different orders and/or concurrently with other blocks fromwhat is depicted and described herein. Moreover, not all illustratedblocks may be required to implement the methods described herein.

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of system 100, thesubsystems and functions of system 200, and method 230 presented inFIGS. 1, 2A, 2B, 2C, and 3. For example, virtualized communicationnetwork 300 can facilitate in whole or in part obtaining a naturallanguage instruction and interpreting it to obtain a machineinterpretation that is analyzed to obtain an intention of the naturallanguage instruction. An actionable command is determined according tothe intention. The actionable command is adapted to cause a digitalmanipulation tool to digitally manipulate a content item to obtain amanipulated content item according to the natural language instruction.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc.,that perform some or all of the functions of network elements 150, 152,154, 156, etc. For example, the network architecture can provide asubstrate of networking capability, often called Network FunctionVirtualization Infrastructure (NFVI) or simply infrastructure that iscapable of being directed with software and Software Defined Networking(SDN) protocols to perform a broad variety of network functions andservices. This infrastructure can include several types of substrates.The most typical type of substrate being servers that support NetworkFunction Virtualization (NFV), followed by packet forwardingcapabilities based on generic computing resources, with specializednetwork technologies brought to bear when general purpose processors orgeneral purpose integrated circuit devices offered by merchants(referred to herein as merchant silicon) are not appropriate. In thiscase, communication services can be implemented as cloud-centricworkloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it iselastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc., to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc., can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc., to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud or might simply orchestrateworkloads supported entirely in NFV infrastructure from thesethird-party locations.

In some embodiments, such as the example system 300, a digital contentdevelopment application 382 is in communication with one or more mobiledevices 384 a, 384 b, . . . 384 n, generally 284 and/or one or morestatic devices, 386 a . . . 386 m, generally 386. The digital contentdevelopment application 382 can be adapted to facilitate in whole or inpart interpretation of a natural language input to determine anintention regarding development of digital content according to thevarious techniques disclosed herein. Likewise, in at least someembodiments, such as the example system 300, a natural languageprocessing service 380 is in communication with one or more of themobile devices 384 and/or static devices, e.g., 386, and/or othercomponents of the communication network virtualized network function,e.g., one or more of the virtual network elements 330, 332, 334. Thedigital content development application 382 can be adapted to facilitatein whole or in part development of digital content according to theactionable commands.

In at least some embodiments, one or more of the wireless access networksystems 120 and/or the broadband access systems 110 includefunctionality, e.g., in the form of any one of an operating system, aclient, a resident app, and combinations thereof. The residentfunctionality can be adapted to perform one or more of the techniquesdisclosed herein, such as interpretation of the natural language inputto determine intention, generation of an actionable command to developdigital content according to the intention, and/or development ofdigital content according to the actionable commands.

The systems, processes and techniques disclosed herein make it easierfor non-experts to create digital content, including animated and/orinteractive content as may be employed in a computer game, or as specialeffects in a television show or movie. For example, a user may entervoice commands, by simply say what they want to do, e.g. “Create a spacewarship scene with 5 ships coming in from the right over a green planet. . . ” It is understood that one or more of the disclosed functionalitymay be implemented with the assistance of one or more of a mobiledevice, e.g., as an app, and/or a desktop computer or workstation. Thefunctionality may be provided to user devices as a resident program, asa client portion of a client-server architecture, as a portal to aservice, e.g., as a Web app, and so on.

By way of further example, a user describes a digital contentdevelopment instruction or command. The user input can be obtainedaccording to a natural language format. Beneficially, according to thetechniques disclosed herein, the natural language input isprogrammatically synthesized into actionable commands suitable for atarget digital content development tool. Thus a command such as “make1000 ships appear” may be synthesized into commands that select and/orgenerate a ship, copy the ship 1000 times and place, or otherwisearrange the ships within a digital development environment.

Accept user speech and turn into actions (this part is not new), e.g.,can use ALEXA services, MS services, Google Speech, IBM Watson, etc. Anynatural language understanding system can be used. Still other systemsuse other methodologies for doing the same without necessarilyconverting waveforms to text—they just act more directly on thewaveform. Microphone/voice may not be necessary for input modes that aretext based, gesture based (e.g., Xbox), mouse point & click, etc.,anything suitable to accept user input to a computer. Anything outsideof natural language-based input, e.g., gestures, may be used as amodifier, e.g., use gesture, such as touch to change size.

Other user inputs, such as “generate terrain,” “forested fill space” maydescribes how to create a land portion of a scene. Responsive to aninterpretation of such natural language commands, selection of a digitalcontent development tool and/or structuring of related actionablecommands may be adapted to use a database of pre-created 3D textureassets, e.g., using TextFab and/or TurboSquid, which provide librariesof such things. The actionable instructions may include “go and grab‘terrain’ from database X.” Alternatively, the actionable instructionsmay be adapted to generate terrain on the fly according toalgorithmically generates terrain, e.g., SpeedTree. In more detail, thedetermined intent may include an action: “generate” using algorithm Y,e.g., SpeedTree or search database X to identify Z. Searchable recordsmay include commercial libraries that provide an API, or internalproprietary libraries.

In some instances, determined intent may include an action:“Modify”+“change scale” to make a 3D object bigger/smaller, e.g.,multiply by a scalar. Other “modification” type actions may include,without limitation, rotate, move, duplicate, delete (“get rid ofthat”=delete). Different types of command types—based on command type,commend is sent to the appropriate fulfillment service, e.g., anidentified on of a number of digital content development tools.

In some instances, determined intent may include an action: contentgenerate command type. Content generation actions may be moresophisticated than content modification commands, which may requirelittle more than querying a library. Still other determined intentionsmay include a procedural animation, e.g., a natural language inputrequesting that a soldier walk from left to right.

Once a “scene” is created/assembled, the scene and/or elements of thescene can be further modified and/or otherwise adapted for generalconsumption. For game applications, the user may want to share adeveloped scene as an interactive content format package or application(middleware packages, such as Unity & Unreal—create a common theme. Forexample, development and/or preparation of a scene may includeapplication of one or more of gameplay dynamics, support for virtualreality, controllers, then can export to one or more differentplatforms: make this a mobile phone app, make this a computer app, makethis an AR app., possibly making modifications before ultimately exportfor use by the different platforms.

By way of an example of passive content a user creates a scene and wantto see a spaceship blowing up a planet. Actionable commands may identifythe scene, the spaceship, and/or the planet. The interpreted action maybe to cause the spaceship to emit a ray, a beam or the like and to haveplanet to explode in reaction to the ray or beam. The explosion eventmay be adapted to occur over a time period identified in the actionablecommands, e.g. 30 seconds, and the results exported or otherwiseprovided for presentation and display as an MP4 file. In theillustrative example, particulars of the explosion, timeframes and thelike may be inferred from one or more of the scene, prior applicationsof similar instructions, user preferences, e.g., according to a userprofile, and/or in response to a direct query: “Please identify durationof explosion in seconds,” to which the user enters a subsequent entry:“30 seconds.” Such queries may include a recommendation or guess thatmay be implemented as a default should a user fail to correct orclarify.

It is envisioned that resulting generated content, e.g., the example MP4file may be shared with friends, posted on Instagram, rendered as animage, not a video. Audio may or may not be inclusive as the case maybe, along with other effects, e.g., vibrating device responsive to gameactions, etc.

It is envisioned that the techniques disclosed herein may be employed byusers experienced in detailed operation of the digital contentdevelopment tools. Such experienced users may utilize the naturallanguage input to obtain a “rough scene” to “block” something out, as astoryboarding tool. If gets some traction, then may stop using voice andrevert to the expertise. Alternatively or in addition, the techniquesdisclosed herein may be used to obtain a relatively rough results thatmay be adjusted according to finer grained controls, or tweaking, usingthe techniques disclosed here and or in cooperation with manual input ofexperienced users.

In the following illustrative example a natural language user input isparsed and/or otherwise interpreted into an action list. Particularactions may be identified and/or categorized. Actions may then beassociated with content development and/or modification resources, whichare manipulated according to actionable commands automatically obtainedfrom the natural language user input. The content development and/ormodification resources respond to the actionable commands to generateand/or modify content based on user input.

For example, a user input to generate terrain may be interpreted and/orotherwise processed to obtain:

Action: Generate

Type: Terrain

Sub-type: . . . .

Other examples of intent may include “generate maps,” “libraryretrieval,” “modify content item,” and/or “delete content item. Userinput may be interpreted to narrow possible intentions according tocertain domains, e.g., terrains, characters. The processes may useexisting tools, e.g., open source tools, or simply define a particulargrouping of words means “generate.” A particular tool may require astrict sequence of words that mean generate that may be interpreted,e.g., using machine learning to create a myriad of different ways that auser input may reduce down to an actionable intent, such as “generate.”It is envisioned that in at least some embodiments, one or more aspectsof the functionality disclosed herein may be implemented in acooperation with an existing natural language processing tool, such asAlexa.

Turning next to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part obtaining a natural language instructionand interpreting it to obtain a machine interpretation that is analyzedto obtain an intention of the natural language instruction. Anactionable command is determined according to the intention. Theactionable command is adapted to cause a digital manipulation tool todigitally manipulate a content item to obtain a manipulated content itemaccording to the natural language instruction.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries, or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

When used in a natural language-based, digital content developmentapplication, the computer 402 can include one or more applications 432that may be adapted to include functionality 486 directed to one or moreof interpretation of a natural language input to determine an intentionof a provider of the natural language input, generation of an actionablecommand to develop digital content according to the intention, and/ordevelopment of digital content according to the actionable commands andaccording to the various techniques disclosed herein.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part obtaining a natural language instruction andinterpreting it to obtain a machine interpretation that is analyzed toobtain an intention of the natural language instruction. An actionablecommand is determined according to the intention. The actionable commandis adapted to cause a digital manipulation tool to digitally manipulatea content item to obtain a manipulated content item according to thenatural language instruction. In one or more embodiments, the mobilenetwork platform 510 can generate and receive signals transmitted andreceived by base stations or access points such as base station oraccess point 122. Generally, mobile network platform 510 can comprisecomponents, e.g., nodes, gateways, interfaces, servers, or disparateplatforms, that facilitate both packet-switched (PS) (e.g., internetprotocol (IP), frame relay, asynchronous transfer mode (ATM)) andcircuit-switched (CS) traffic (e.g., voice and data), as well as controlgeneration for networked wireless telecommunication. As a non-limitingexample, mobile network platform 510 can be included intelecommunications carrier networks and can be considered carrier-sidecomponents as discussed elsewhere herein. Mobile network platform 510comprises CS gateway node(s) 512 which can interface CS traffic receivedfrom legacy networks like telephony network(s) 540 (e.g., publicswitched telephone network (PSTN), or public land mobile network (PLMN))or a signaling system #7 (SS7) network 560. CS gateway node(s) 512 canauthorize and authenticate traffic (e.g., voice) arising from suchnetworks. Additionally, CS gateway node(s) 512 can access mobility, orroaming, data generated through SS7 network 560; for instance, mobilitydata stored in a visited location register (VLR), which can reside inmemory 530. Moreover, CS gateway node(s) 512 interfaces CS-based trafficand signaling and PS gateway node(s) 518. As an example, in a 3GPP UMTSnetwork, CS gateway node(s) 512 can be realized at least in part ingateway GPRS support node(s) (GGSN). It should be appreciated thatfunctionality and specific operation of CS gateway node(s) 512, PSgateway node(s) 518, and serving node(s) 516, is provided and dictatedby radio technology(ies) utilized by mobile network platform 510 fortelecommunication over a radio access network 520 with other devices,such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc., that perform particulartasks and/or implement particular abstract data types.

When used in a natural language-based, digital content developmentapplication, the embodiment 500 of the mobile network platform 510 mayinclude one or more of functionality 589 at the server(s) 514,functionality 588 operational within the RAN 520 and/or functionality584 operational within the mobile device 575. One or more of thedisclosed functionalities 584, 588, 589 can be adapted to processnatural language input to determine intent and/or to generate one ormore actionable commands to develop digital content according to theintent. Alternatively or in addition, one or more of the disclosedfunctionalities 584, 588, 589 can be adapted to develop digital content,including one or more of digital content generation and manipulation ormodification, according to the various techniques disclosed herein.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part obtaining anatural language instruction and interpreting it to obtain a machineinterpretation that is analyzed to obtain an intention of the naturallanguage instruction. An actionable command is determined according tothe intention. The actionable command is adapted to cause a digitalmanipulation tool to digitally manipulate a content item to obtain amanipulated content item according to the natural language instruction.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive, or other forms of sensing technology to detecthow much surface area of a user's finger has been placed on a portion ofthe touch screen display. This sensing information can be used tocontrol the manipulation of the GUI elements or other functions of theuser interface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high-volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The digital content development techniques disclosed herein can beapplied to a wide variety of applications and/or digital content typesand mediums including but not limited to games, movies, television,internet video, internet content, virtual reality, augmented reality,mixed reality, and advertising content. For example, the tool may beutilized as part of an interactive advertisement in which naturallanguage input from a consumer is obtained and used to generate and/ormodify digital content of an advertisement. The interactiveadvertisement may be placed in an ad space, e.g., in an Over-The-TopVideo stream of a movie. In such an application, the “interactive”aspect may allow for adjustment of the advertisement, such as a scene,an object and/or character within the scene in near-real-time.

As an example, a voice instruction for a car commercial may causechanges to a make or model of a car and/or a characteristic of thevehicle, such as its color that may be shown in interactiveadvertisement. The natural language input may be obtained in response tosystem prompts, e.g., questions, to which a user may respond.Alternatively or in addition, the natural language input may be obtainedthrough consumer dialogue and/or commentary as may be captured during aparticular viewing session and/or over an extended period of time thatmay include many different viewing sessions. In some embodiments,natural language may be obtained by other systems, such as personal,home, or vehicle digital assistances adapted to capture spoken input.Such natural language input may be analyzed by the system, e.g., todetermine preferences, interests, likes and/or dislikes that may be usedby the system in developing digital content in a general sense, e.g., tobe used by the system in any guesses that may be employed. In otherinstances, the system may use any natural language captured by thesystem and/or by other systems, to anticipate future instructionsrelated to development of digital content. Such anticipatory action mayinclude generation of scenes, characters, objects, association ofbrands, and so on.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only and doesnot otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

In at least some embodiments, the disclosed subject matter may beutilized as an interface in a broad range of device platforms including,without limitation, desktop computers, mobile devices of all kindsincluding smartphones, tablets, wearables, augmented reality (AR)headsets, and virtual reality (VR) headsets. It is understood that thedisclosed subject matter may utilize a microphone or other devicecapable of capturing vocal utterances as may be available within suchdevice platforms and/or added thereto. Output signals from such vocalcapturing devices may be sent to a natural language processing systemthat may be resident on the device and/or running remotely, e.g., over anetwork as a server resource. A networked server resource may includeany type of network, e.g., locally, centralized, and/or at a networkedge.

In at least some embodiments, the disclosed subject matter may utilize avirtual assistant, or so-called, smart speaker, such as the Alexa®voice-controlled information device, a trademark of Amazon Technologies,Inc., Seattle, Wash., and/or smart speaker and/or screens, such as Nest®audio input devices, a trademark of Google, Inc., Mountain View, Calif.In at least some embodiments, the audio input device may not be nativelyintegrated into a device, such as a device platform operating accordingto the disclosed subject matter. For example, the audio input mayinclude microphone that may be pluggable into and/or otherwiseconnectable to such a platform.

In at least some embodiments, input may include audio input, e.g.,vocalization and/or non-vocal utterances. It is understood that, withoutlimitation, such audio input may include recorded human voice sequencesthat may be used, e.g., to generate textual input. These could berecorded by the user or they could come from other sources.Alternatively or in addition, audio input may include synthesized audio.For instances where audio input is used for a natural languageprocessing system, any vocal utterance audio may be synthesized by acomputational process. For example, AI and/or a computational drivenentity, e.g., controlled by rules, scripts or other method, may generatea vocal utterance. By way of example and without limitation, AI and/orcomputational driven entities may include one or more of softwareagents, avatars, non-player characters, and/or bots of all types.

In at least some embodiments, devices, systems, processes and the like,operating according to the disclosed subject matter, may operateexclusively according to a non-vocal, e.g., a text input-basedinterface. Example text-based interfaces may include, withoutlimitation, command line interfaces, graphic user interfaces (GUI's)and/or text based communications applications and services such as SMSand messaging applications, such as WhatsApp® instant messagingsoftware, a trademark of WhatsApp Inc., Menlo Park, Calif., FacebookMessenger, and so on.

It is envisioned that in at least some embodiments, a source of input,e.g., text does not need to be human generated. For example, textualinput may be generated and/or synthesized by a computational process.For example, a user wants to create a visual scene similar to or as seenin a photo or a video sequence. The user may send the photo and/or videosequence to an application and/or service, e.g., that runs AI-basedanalysis of the photo and/or video sequence. The analysis may generate adescriptive text string that may be processed according to the disclosedsubject matter into content creation instructions. In at least someembodiments, the text generated by the AI analysis is human readable.Alternatively or in addition, text generated by the AI analysis is notentirely human readable. In at least some embodiments, the input doesnot necessarily need to be text, e.g., including machine readable codeof any type. Such machine-readable code and/or commands may be providedas direct API and/or system calls to a content creation system. A sourceof the input, e.g., text, may be from a movie script, a book, or otherdocument. The textual and/or non-textual system input may also begenerated by an AI or computational driven entity, e.g., controlled byrules, scripts and/or other suitable method. Examples of such entitiesincludes, without limitation, one or more of agents, avatars, non-playercharacters, and bots of all types.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

It is understood that the disclosed subject matter may function with anytype of software application and/or service, including applicationsand/or services running on any of the example devices and/or systemsdisclosed herein, inclusive of mobile device apps, desktop applications,and the like, whether downloaded and/or obtained via physical mediaand/or other type of storage. It is envisioned that in at least someembodiments, devices, systems, processes and the like, operatingaccording to the disclosed subject matter, may run within and/orinterface with a cloud-based application. In one or more embodiments,information regarding use of services can be generated includingservices being accessed, media consumption history, user preferences,and so forth. This information can be obtained by various methodsincluding user input, detecting types of communications (e.g., videocontent vs. audio content), analysis of content streams, sampling, andso forth. The generating, obtaining, and/or monitoring of thisinformation can be responsive to an authorization provided by the user.In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, . . . ,xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches, and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A method, comprising: obtaining, by a processingsystem including a processor, a machine-readable input comprising anatural language instruction; analyzing, by the processing system, themachine-readable input to obtain an intention of the natural languageinstruction; and identifying, by the processing system, a scriptedcommand of a native scripting language of a digital manipulation tooladapted to digitally manipulate a digital content item to obtain adigitally manipulated content item according to the intention of thenatural language instruction when applied to the digital manipulationtool.
 2. The method of claim 1, wherein the machine-readable inputcomprises a textual representation of a natural language instructionspoken by a human.
 3. The method of claim 1, wherein themachine-readable input is obtained without obtaining a speech-to-texttranslation of a natural language instruction spoken by a human.
 4. Themethod of claim 1, wherein the analyzing of the machine-readable inputfurther comprises using a natural language processor to obtain afunctional understanding of the machine-readable input, wherein theintention is obtained according to the functional understanding.
 5. Themethod of claim 1, further comprising identifying, by the processingsystem, the digital manipulation tool according to the machine-readableinput.
 6. The method of claim 1, wherein the scripted command comprisesa creation command, the digitally manipulated content item comprising anewly created content element responsive to the natural languageinstruction.
 7. The method of claim 1, wherein the scripted commandcomprises a modification command, the digitally manipulated content itemcomprising a modified content element responsive to the natural languageinstruction.
 8. The method of claim 1, wherein the scripted command ofthe native scripting language comprises an animation command, thedigitally manipulated digital content item comprising an animatedcontent element responsive to the natural language instruction.
 9. Themethod of claim 1, wherein the obtaining the machine-readable inputcomprising the natural language instruction further comprises:receiving, by the processing system, an audio signal comprising thenatural language instruction.
 10. The method of claim 9, wherein theaudio signal comprises one of a baseband audio signal, a radio frequencywireless signal, or a combination thereof.
 11. A device, comprising: aprocessing system including a processor; and a memory that storesexecutable instructions that, when executed by the processing system,facilitate performance of operations, the operations comprising:obtaining an input signal comprising a natural language instruction;analyzing the input signal to obtain an intention of the naturallanguage instruction; and determining a scripted command of a nativescripting language of a digital manipulation tool adapted to digitallymanipulate a digital content item according to the intention of thenatural language instruction when applied to the digital manipulationtool.
 12. The device of claim 11, wherein the operations furthercomprise: converting the input signal comprising the natural languageinstruction into a textual representation of the natural languageinstruction.
 13. The device of claim 11, wherein the analyzing the inputsignal to obtain the intention of the natural language instruction isaccomplished without obtaining a speech-to-text translation of the inputsignal.
 14. The device of claim 11, wherein the analyzing of the inputsignal comprising the natural language instruction further comprisesusing a natural language processor to obtain a functional understandingof the input signal comprising the natural language instruction, whereinthe intention is obtained according to the functional understanding. 15.The device of claim 11, wherein the determining the scripted command isaccording to predetermined operational requirements of the digitalmanipulation tool.
 16. The device of claim 11, wherein the scriptedcommand comprises a creation command adapted to generate a newly createdcontent element selected from a group consisting of a scenic element ofa digitally represented environment, an object element adapted forpresentation within the digitally represented environment, an actorelement adapted for animation within the digitally representedenvironment, and any combination thereof.
 17. A non-transitory,machine-readable medium, comprising executable instructions that, whenexecuted by a processing system including a processor, facilitateperformance of operations, the operations comprising: obtaining anatural language instruction; analyzing the natural language instructionto obtain an intention of the natural language instruction; andidentifying a scripted command of a native scripting language of adigital manipulation tool adapted to digitally manipulate a content itemaccording to the intention of the natural language instruction whenapplied to the digital manipulation tool.
 18. The non-transitory,machine-readable medium of claim 17, wherein the analyzing the naturallanguage instruction further comprises obtaining a textualrepresentation of the natural language instruction.
 19. Thenon-transitory, machine-readable medium of claim 17, wherein theanalyzing the natural language instruction is accomplished withoutobtaining a speech-to-text translation of the natural languageinstruction.
 20. The non-transitory, machine-readable medium of claim17, wherein the analyzing of the natural language instruction furthercomprises using a natural language processor to obtain a functionalunderstanding of the natural language instruction, wherein the intentionis obtained according to the functional understanding.